Torsten Caesar

Sophisticated topology of hidden Markov models for cursive script recognition

The paper describes an adaptation of hidden Markov models (HMM) to automatic recognition of unrestricted handwritten words. Many interesting details of a 50,000 vocabulary recognition system for US city names are described. This system includes feature extraction, classification, estimation of model parameters, and word recognition. The feature extraction module transforms a binary image to a sequence of feature vectors. The classification module consists of a transformation based on linear discriminant analysis and Gaussian soft-decision vector quantizers which transform feature vectors into sets of symbols and associated likelihoods. Symbols and likelihoods form the input to both HMM training and recognition. HMM training performed in several successive steps requires only a small amount of gestalt labeled data on the level of characters for initialization. HMM recognition based on the Viterbi algorithm runs on subsets of the whole vocabulary.<<ETX>>

Estimating the baseline for written material

Estimation of aiding typographical rulers is a challenging task especially for everyday handwriting. The method described here performs very well as long as the model assumption of one straight line is satisfied. It is completely based on contour processing. A sophisticated iterated regression analysis processing weighted points is the central algorithm. The method can be used for handwriting as well as machine printed texts without adjusting the parameters.

Using lexical knowledge for the recognition of poorly written words

Handwriting recognition systems usually need the support of lexical knowledge in order to achieve acceptable results. Lexicons of practical applications are often very large which results in prohibitive run time and recognition performance. So there is a need to reduce large lexicons efficiently without loosing the correct entry. Often it is possible to recognize some isolated or resegmented characters of a word but not the whole word. These recognition results may be used as hints for an initial lexicon reduction. In order to use these hints techniques are needed which are able to handle character alternatives as well as touched and broken characters. The article discusses lexicon techniques in respect to their efficiency and robustness. A hybrid approach is proposed which reduces large lexicons efficiently and shows a robust behavior when broken and touched characters are observed.

Handwriting Recognition by Statistical Methods

In January 1992 a project was started which is focused on the recognition of handwritten words, constraint by a given lexicon. The target application is the recognition of US city names in address reading systems.

New features for Chinese character recognition

The wide range of shape variations for Chinese characters requires an adequate representation of the discriminating features for classification. For the recognition of Latin characters or numerals pixel values of a normalized raster image are proper features to reach very good recognition rates. But Chinese characters require a much higher resolution of the normalized raster image to enable a discrimination of complex shaped characters which leads to a feature space dimensionality of prohibitive computational effort for classification. Therefore feature extraction algorithms are needed which capture the discriminative characteristics of character shapes in a compact form. Several algorithms were proposed in the past and many of them are based on the contour data. This paper also introduces a contour based approach which is very time efficient and overcomes the problem of vanishing lines during anisotropic size normalization.

Preprocessing raw binary images by means of contours

In a binary image contours may be seen as the discriminating curve between objects and background. Contours of connected components are always a Jordan curve. One symbol (e.g., a character) may consist of more than one such curve. Processing these curves is a one-dimensional task. Almost all common processing steps can be designed to work on contours rather than on the two-dimensional image. Moreover, contour processing gives new insight to well know problems and enables new processing steps or produces more information about the relations between connected components or objects of the image. The authors present preprocessing operations which work directly on the level of contours. Compared to the corresponding iconic operations, algorithms working on the contour level are mostly more efficient. Based on the contours of the connected components methods for filtering and slant normalization are described.

Utilization of large disordered sample sets for classifier adaptation in complex domains

A methodology for structuring large disordered sample sets for classifiers is presented. The object-oriented framework is an essential part of this methodology. Classes can be viewed as sets, and sets again can be viewed as objects. For this reason, operations and techniques from both domains (sets and OO technology) can be utilized to set up a system for computer-aided labeling. Since labeling is a time-consuming task, the handling of the system has to support efficient labeling. A second important aspect of the application is easy system handling to allow inexperienced examiners to use the system.<<ETX>>